Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

2018-04-24 · Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, Edward Chow

Transcribing voice communications in NASA's launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

📄 PDF Abstract BibTeX arXiv:1804.09552

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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data AugmentationLanguage ModelingLanguage Modelling speech-recognition Speech Recognition

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